Automated Monitoring For Construction Productivity Recognition

2021 ◽  
Author(s):  
Khalid Mhmoud Alzubi ◽  
Wesam Salah Alaloul ◽  
Marsail Al Salaheen ◽  
Abdul Hannan Qureshi ◽  
Muhammad Ali Musarat ◽  
...  
Keyword(s):  
Construction Productivity ◽  
Automated Monitoring

Productivity monitoring in building construction projects: a systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Khalid M. Alzubi ◽  
Ahmad B. Malkawi ◽  
Marsail Al Salaheen ◽  
Muhammad Ali Musarat
Keyword(s):  
Systematic Review ◽  
Construction Projects ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Building Construction ◽  
Content Type ◽  
Construction Productivity ◽  
Automated Monitoring ◽  
Monitoring Tools ◽  
Meta Analyses

PurposeThe unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.Design/methodology/approachThis study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.FindingsA detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.Originality/valueThis review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.

THE SYSTEM OF AUTOMATED MONITORING INFORMATION PROTECTION FOR TESTING ELECTRONIC DEVICES IN A CLIMATIC CELL

2012 ◽  
Vol 71 (6) ◽  
pp. 539-545
Author(s):  
V. N. Lopin ◽  
N. S. Kobelev ◽  
D. V. Avdyakov ◽  
T. S. Rozhdestvenskaya ◽  
V. N. Kobelev ◽  
...  
Keyword(s):  
Electronic Devices ◽  
Information Protection ◽  
Automated Monitoring ◽  
Monitoring Information

Construction productivity indices in socialism compared with capitalism

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Alan Todhunter ◽  
◽  
Mark Crowley ◽  
Farid Sartipi ◽  
◽  
...  
Keyword(s):  
Construction Productivity ◽  
Productivity Indices

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction

THE AUTOMATED MONITORING OF CARDIO VASCULAR DISEASES RISK FACTORS

2008 ◽  
Vol 9 (1) ◽  
pp. 91
Author(s):  
N.V. Nasonova ◽  
L.V. Sherbakova
Keyword(s):  
Risk Factors ◽  
Vascular Diseases ◽  
Automated Monitoring ◽  
Cardio Vascular

scholarly journals Implementing an automated monitoring process in a digital, longitudinal observational cohort study

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Lisa Lindner ◽  
Anja Weiß ◽  
Andreas Reich ◽  
Siegfried Kindler ◽  
Frank Behrens ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Data Quality ◽  
Missing Values ◽  
Randomized Clinical Trials ◽  
Cost Effective ◽  
Observational Research ◽  
Research Centre ◽  
Automated Monitoring ◽  
Monitoring Process ◽  
Observational Cohort

Abstract Background Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research. Methods An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried. Results Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis. Conclusion Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.

Development of formwork automation design software for improving construction productivity

2021 ◽  
Vol 126 ◽  
pp. 103680
Author(s):  
Bokyeong Lee ◽  
Hyeonggil Choi ◽  
Byongwang Min ◽  
Jungrim Ryu ◽  
Dong-Eun Lee
Keyword(s):  
Construction Productivity ◽  
Design Software
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